cs.RO（2024-06-24）

📊 共 10 篇论文

🎯 兴趣领域导航

支柱一：机器人控制 (Robot Control) (5) 支柱二：RL算法与架构 (RL & Architecture) (3) 支柱七：动作重定向 (Motion Retargeting) (1) 支柱九：具身大模型 (Embodied Foundation Models) (1)

🔬 支柱一：机器人控制 (Robot Control) (5 篇)

#	题目	一句话要点	标签	🔗	⭐
1	Socially Acceptable Bipedal Robot Navigation via Social Zonotope Network Model Predictive Control	提出基于社交Zonotope网络MPC的双足机器人社交导航方法	legged robot bipedal biped
2	QuadrupedGPT: Towards a Versatile Quadruped Agent in Open-ended Worlds	QuadrupedGPT：面向开放环境的多功能四足机器人代理	quadruped locomotion locomotion policy
3	Dreamitate: Real-World Visuomotor Policy Learning via Video Generation	Dreamitate：通过视频生成实现真实世界中的视觉运动策略学习	manipulation policy learning behavior cloning
4	Musculoskeletal AutoEncoder: A Unified Online Acquisition Method of Intersensory Networks for State Estimation, Control, and Simulation of Musculoskeletal Humanoids	提出基于Musculoskeletal AutoEncoder的统一框架，用于肌肉骨骼人形机器人的状态估计、控制和仿真。	humanoid
5	Multi-Objective Global Path Planning for Lunar Exploration With a Quadruped Robot	针对月球探测四足机器人，提出多目标全局路径规划方法	quadruped

🔬 支柱二：RL算法与架构 (RL & Architecture) (3 篇)

#	题目	一句话要点	标签	🔗	⭐
6	RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning	提出基于光线追踪和复合模仿学习的多无人机避障方法	PPO imitation learning behavior cloning
7	MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention	提出MEReQ，通过最大熵残差Q逆强化学习实现人机交互中的高效策略对齐	reinforcement learning imitation learning inverse reinforcement learning
8	Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the Predictive Model of Sensor State Transition	提出基于传感器状态转移预测模型的柔性肌腱手工具使用稳定控制方法	predictive model

🔬 支柱七：动作重定向 (Motion Retargeting) (1 篇)

#	题目	一句话要点	标签	🔗	⭐
9	neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction	neuROSym：基于ROS的神经符号模型，用于提升机器人环境中的人类运动预测	human motion human motion prediction motion prediction

🔬 支柱九：具身大模型 (Embodied Foundation Models) (1 篇)

#	题目	一句话要点	标签	🔗	⭐
10	CogExplore: Contextual Exploration with Language-Encoded Environment Representations	CogExplore：利用语言编码环境表征进行上下文感知的机器人探索	large language model

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